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MLflow with a Silicon Valley Engineer

Still managing ML experiments manually? 🤔 Automate everything from experiment tracking to model deployment with MLflow and dramatically boost development productivity! A must-have skill for data scientists and ML engineers 💡 Start now! 🚀

(5.0) 7 reviews

78 students

mlflow
Machine Learning(ML)
mlops
Deep Learning(DL)
AI

This course is prepared for Beginners.

What you will learn!

  • Model version control

  • Model pipeline optimization

  • ML Workflow Optimization

  • Model Experiment Tracking

Master MLflow: From Tracking Machine Learning Experiments to Deploying Models!

MLflow is used by Uber, Databricks, and Microsoft It is used by many companies and is an essential tool for data scientists and ML engineers to develop and deploy models efficiently .

#mlflow, #machine learning, #mlops, #deep learning, #artificial intelligence (AI)

  • Track and manage experiments using MLflow


  • Automated model version management and deployment

  • How to run machine learning workflows efficiently

  • Improve reproducibility and productivity of ML projects

From model experimentation to deployment, all in one place! Transform your machine learning workflow with MLflow. 🔥

💡 Lecture Planning Background

To apply machine learning to real services , model experiment tracking, performance comparison, and deployment automation are essential. However, many developers and data scientists often experience confusion because they do not keep experimental records in Excel or manage model versions .

This course will teach you how to systematically manage experiments using MLflow and build an efficient model deployment process through MLOps .

Get started today to automate your ML experiments and maximize your AI project productivity ! 🚀

🏛 Learn about these things

Core components of MLflow

Learn the core features of MLflow to easily implement experiment tracking, model management, and deployment automation . Learn how to increase the efficiency and reproducibility of your ML projects through hands-on practice!

MLflow Key features

Machine Learning Life Cycle

Understand the full life cycle of machine learning models from development to deployment, and learn how to leverage MLflow at each stage to track experiments, manage models, and automate operations.

Machine Learning Life Cycle

It's good to listen together 🧑🏻‍🏫

331680

Data Science Fundamentals with Silicon Valley Engineers

Data science is hot these days. Learn and use it in your own way! 💡
Gain hands-on experience from data analysis to algorithm implementation with essential tools like Anaconda, Numpy, Pandas, and Scikit-learn!
Learn how to gain insights and solve problems with data in a fun and easy way. 🎯

🤔 Things to note before taking the class

Practice environment

  • Operating System and Version (OS): macOS, Linux, Windows + Docker


  • PC specifications

    • CPU: 4 cores or more

    • RAM: 8GB

    • Storage: 20GB or more of free space (for storing Docker images & data)

    • Docker: Docker Desktop or Docker Engine

Learning Materials

  • We provide PDF lecture materials (refer to each video learning material) and code materials.

Player Knowledge and Notes

  • This lecture lab is set up with Docker. If you want to learn more about Docker, I recommend you refer to my free Docker lecture . Lecture link: [ https://inf.run/8eFCL ]

  • If you have any questions during the class, please feel free to leave them. However, since I am located in the western United States, it may take some time for me to respond.

Recommended for
these people!

Who is this course right for?

  • Data scientist who wants to systematically manage ML experiments

  • ML Engineers who need automated model deployment and management.

  • Developers who want to build reproducible ML workflows

  • AI/ML practitioners who want to apply MLflow in practice

  • Someone who wants to make data-driven decisions efficiently.

Need to know before starting?

  • Python - Basic Syntax and Library Usage

  • Understanding Machine Learning: Model Training and Evaluation Concepts

  • Pandas & NumPy – Data Processing and Analysis

Hello
This is altoformula

9,099

Students

554

Reviews

293

Answers

4.8

Rating

24

Courses

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  • 🫡 똑똑하지는 않지만, 포기하지 않고 꾸준히 하면 뭐든지 이룰수 있다는 점을 꼭 말씀드리고 싶습니다. 항상 좋은 자료로 옆에서 도움을 드리겠습니다

 

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Curriculum

All

21 lectures ∙ (2hr 44min)

Course Materials:

Lecture resources
Published: 
Last updated: 

Reviews

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7 reviews

5.0

7 reviews

  • yuki님의 프로필 이미지
    yuki

    Reviews 9

    Average Rating 4.9

    Edited

    5

    100% enrolled

    회사에서 MLOps를 구축하는데 큰 도움이 되었습니다 ㅎㅎ 유데미, 인프런, 패스트캠퍼스를 비롯한 여러 강의를 들었지만, 이 강의가 제일 도움 많이되었습니다 :) 미국 엔지니어님의 여러 강의를 들었지만, 항상 합리적인 가격과 적절한 분량에 강의를 제공해주시는 것 같아 좋습니다.

    • 안녕하세요 yuki님, 시간내서 좋은 리뷰 남겨주셔서 감사합니다! 강의가 도움이 되었다니 다행입니다!

  • 성현님의 프로필 이미지
    성현

    Reviews 11

    Average Rating 5.0

    5

    62% enrolled

    중반 정도 듣고 강의평을 남깁니다. MLflow 사용에 있어서 중요한 부분들을 친절하게 잘 설명해주셔서 인상깊습니다. 나머지 강의 부분도 잘 듣고 업무에 활용해야겠네요. 좋은 강의를 저렴한 가격에 제공해주셔서 감사합니다.

    • 안녕하세요 성현님, 시간내서 좋은 리뷰 남겨주셔서 감사합니다. 열심히 노력한 보람이 있네요. 누군가에게 도움이 된 강의라는 점이 뿌듯합니다

  • XL galasy님의 프로필 이미지
    XL galasy

    Reviews 3

    Average Rating 5.0

    5

    100% enrolled

    MLOps 관련 플랫폼을 구축하는 데 많은 도움이 되었습니다. 이 강의를 계기로 ML/DL 등 다양한 분야에 사용하기 위해 공부할 수 있었습니다.

    • 안녕하세요 XL galasy님, 시간내서 좋은 리뷰 남겨주셔서 감사합니다!

  • qiulong Xin님의 프로필 이미지
    qiulong Xin

    Reviews 16

    Average Rating 4.9

    5

    33% enrolled

    MLflow를 막연히 알고만 있었는데, 강의를 들으면서 실무에서 어떻게 활용할 수 있을지 감이 잡혔습니다. 구성도 깔끔하고 예제도 잘 되어 있어서 따라가기 쉬웠어요. MLOps에 관심 있는 분들께 강추합니다!

    • 안녕하세요 qiulong Xin님, 시간내서 좋은 리뷰 남겨주셔서 감사합니다!

  • 동환님의 프로필 이미지
    동환

    Reviews 1

    Average Rating 5.0

    5

    38% enrolled

    MLflow 의 기본적인 것을 가르쳐 주십니다.

    • 안녕하세요 동환님, 시간내서 좋은 리뷰 남겨주셔서 감사합니다!

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